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The Journal of Neuroscience, March 2, 2005, 25(9):2312-2321; doi:10.1523/JNEUROSCI.4795-04.2005
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Behavioral/Systems/Cognitive
Spike-Frequency Adaptation Separates Transient Communication Signals from Background Oscillations
Jan Benda,
André Longtin, and
Len Maler
Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, K1H 8M5 Canada
Spike-frequency adaptation is a prominent feature of many neurons. However, little is known about its computational role in processing behaviorally relevant natural stimuli beyond filtering out slow changes in stimulus intensity. Here, we present a more complex example in which we demonstrate how spike-frequency adaptation plays a key role in separating transient signals from slower oscillatory signals. We recorded in vivo from very rapidly adapting electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus. The firing-frequency response of electroreceptors to fast communication stimuli ("small chirps") is strongly enhanced compared with the response to slower oscillations ("beats") arising from interactions of same-sex conspecifics. We are able to accurately predict the electroreceptor afferent response to chirps and beats, using a recently proposed general model for spike-frequency adaptation. The parameters of the model are determined for each neuron individually from the responses to step stimuli. We conclude that the dynamics of the rapid spike-frequency adaptation is sufficient to explain the data. Analysis of additional data from step responses demonstrates that spike-frequency adaptation acts subtractively rather than divisively as expected from depressing synapses. Therefore, the adaptation dynamics is linear and creates a high-pass filter with a cutoff frequency of 23 Hz that separates fast signals from slower changes in input. A similar critical frequency is seen in behavioral data on the probability of a fish emitting chirps as a function of beat frequency. These results demonstrate how spike-frequency adaptation in general can facilitate extraction of signals of different time scales, specifically high-frequency signals embedded in slower oscillations.
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Figure 7. Comparison of the response-gain data with model predictions and behavior. A, The gain Equation 3 of the high-pass filter generated by adaptation (solid line) as a function of stimulus frequency. The averaged value of the measured effective adaptation time constants sets the cutoff frequency fcutoff of the gain function to 23 Hz (vertical line in all panels). Chirps are high-frequency signals (gray area) that are transmitted with a high gain. B, The response gain Equation 6 as a function of positive beat frequencies  f estimated from the high-pass filter shown in A. The dashed line is the response gain for 14-ms-wide chirps that generate a phase shift of 1, and the gray area is for phase shifts ranging from 0.25 to 1.5. C, The response gain from B (dashed line and gray area) explains the decay of the observed response gain only qualitatively (filled circles; median with 2nd and 3rd quartiles). For the 18 cells in which f-I curves were measured, we computed the response gains as predicted by the models. Using the adaptation model Equation 1, thus taking the saturating f-I curves into account, does not improve the match (squares). However, the additional low-pass filter properties introduced by spikes, modeled using the perfect integrator Equation 4, reduce the predicted response gains significantly (triangles), resulting in a much better match to the actually observed data. The variability of the response-gain data can be mainly attributed to the different sized chirps (compare error bars to the width of the gray area). D, The probability of a male fish emitting chirps as a function of beat frequency as reported by Bastian et al. (2001 ), their Fig. 3A.
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Key words: adaptation; high-pass filter; communication; receptor neuron; electrosensory system; weakly electric fish
Received Nov 24, 2004;
revised January 21, 2005;
accepted January 22, 2005.
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